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added some pure-python performance optimizations
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‎docs/optimizations.md‎

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# Performance Optimizations
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This document describes performance optimizations applied to Entangled's core parsing and tangling operations. These changes improve throughput by approximately 30% with no changes to functionality or external API.
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## Summary
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The primary optimizations are:
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1. **Pre-compile regex patterns at module level** instead of compiling on every function call
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2. **Use list accumulation with `"".join()`** instead of `O(n²)` string concatenation
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3. **Cache dynamically-generated regex patterns** to avoid repeated compilation
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These are standard Python optimization techniques that require no additional dependencies.
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## Background
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Profiling identified that a significant portion of execution time was spent in:
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- Regex compilation (`re.match()` with string patterns compiles on every call)
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- String concatenation in loops (`text += line` creates a new string each iteration)
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### Profiling Methodology
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A realistic benchmark was created simulating a literate programming project with:
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- 17 markdown files
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- ~5,000 lines of content
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- 365 code blocks with nested references
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The benchmark measured the full load-and-tangle workflow across multiple iterations.
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## Optimizations Applied
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### 1. Pre-compiled Regex in `model/tangle.py`
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The `naked_tangler()` function matches every line against a reference pattern (`<<refname>>`). Previously, the regex was compiled on each call to `re.match()`.
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**Before:**
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```python
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def naked_tangler(refs: ReferenceMap) -> Tangler:
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def tangler(...) -> Generator[str]:
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for line in lines(code_block.source):
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# Compiles regex on EVERY line
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if m := re.match(r"^(?P<indent>\s*)<<(?P<refname>[\w:/_.-]+)>>\s*$", line.rstrip()):
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...
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```
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**After:**
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```python
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# Compiled once at module load
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_REF_PATTERN = re.compile(r"^(?P<indent>\s*)<<(?P<refname>[\w:/_.-]+)>>\s*$")
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def naked_tangler(refs: ReferenceMap) -> Tangler:
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def tangler(...) -> Generator[str]:
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for line in lines(code_block.source):
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# Uses pre-compiled pattern
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if m := _REF_PATTERN.match(line.rstrip()):
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...
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```
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### 2. Pre-compiled Regex in `readers/code.py`
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The `open_block()` and `close_block()` functions parse annotated code files during stitch operations.
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**Before:**
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```python
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OPEN_BLOCK_EXPR = r"^(?P<indent>\s*).* ~/~ begin <<..."
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def open_block(line: str) -> OpenBlockData | None:
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if not (m := re.match(OPEN_BLOCK_EXPR, line)): # Compiles every call
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return None
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```
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**After:**
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```python
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_OPEN_BLOCK_PATTERN = re.compile(
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r"^(?P<indent>\s*).* ~/~ begin <<..."
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)
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def open_block(line: str) -> OpenBlockData | None:
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if not (m := _OPEN_BLOCK_PATTERN.match(line)): # Uses compiled pattern
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return None
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```
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### 3. Cached Regex in `parsing.py`
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The parser combinator functions `matching()` and `fullmatch()` create regex patterns dynamically. A module-level cache avoids recompiling the same patterns.
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**Before:**
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```python
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def matching(regex: str) -> Parser[tuple[str, ...]]:
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pattern = re.compile(f"^{regex}") # Compiles every time matching() is called
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...
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```
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**After:**
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```python
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_pattern_cache: dict[str, re.Pattern[str]] = {}
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def _cached_pattern(regex: str) -> re.Pattern[str]:
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if regex not in _pattern_cache:
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_pattern_cache[regex] = re.compile(f"^{regex}")
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return _pattern_cache[regex]
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def matching(regex: str) -> Parser[tuple[str, ...]]:
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pattern = _cached_pattern(regex) # Returns cached compiled pattern
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...
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```
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### 4. Cached Regex in `hooks/quarto_attributes.py`
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The `split_yaml_header()` function generates patterns based on language comment syntax. These are now cached per comment style.
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**Before:**
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```python
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def split_yaml_header(language: Language, source: str) -> tuple[str, str, object]:
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trigger: str = re.escape(language.comment.open) + r"\s*\|(.*)"
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for i, line in enumerate(lines):
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if m := re.match(trigger, line): # Compiles on every line
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...
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```
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**After:**
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```python
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_yaml_header_pattern_cache: dict[str, re.Pattern[str]] = {}
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def _get_yaml_header_pattern(comment_open: str) -> re.Pattern[str]:
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if comment_open not in _yaml_header_pattern_cache:
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pattern = re.escape(comment_open) + r"\s*\|(.*)"
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_yaml_header_pattern_cache[comment_open] = re.compile(pattern)
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return _yaml_header_pattern_cache[comment_open]
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def split_yaml_header(language: Language, source: str) -> tuple[str, str, object]:
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pattern = _get_yaml_header_pattern(language.comment.open)
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for i, line in enumerate(lines):
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if m := pattern.match(line): # Uses cached pattern
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...
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```
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### 5. Efficient String Building in `model/tangle.py`
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The `tangle_ref()` function accumulated output using `+=` concatenation, which is `O(n²)` for n lines.
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**Before:**
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```python
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def tangle_ref(refs, name, annotation) -> tuple[str, set[PurePath]]:
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out = ""
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ref_lst = refs.select_by_name(name)
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for line in tangler(tangler, deps, ref_lst[0], False, True):
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out += line # O(n²) - creates new string each iteration
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for ref in ref_lst[1:]:
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for line in tangler(tangler, deps, ref, False, False):
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out += line
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return out, deps
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```
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**After:**
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```python
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def tangle_ref(refs, name, annotation) -> tuple[str, set[PurePath]]:
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def all_lines():
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ref_lst = refs.select_by_name(name)
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yield from tangler(tangler, deps, ref_lst[0], False, True)
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for ref in ref_lst[1:]:
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yield from tangler(tangler, deps, ref, False, False)
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out = "".join(all_lines()) # O(n) - single allocation
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return out, deps
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```
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### 6. Efficient String Building in `readers/code.py`
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The `read_block()` function accumulated content similarly.
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**Before:**
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```python
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content = ""
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while input:
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...
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content += line # O(n²)
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```
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**After:**
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```python
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content_parts: list[str] = []
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while input:
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...
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content_parts.append(line) # O(1) amortized
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...
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yield Block(block_data.ref, "".join(content_parts)) # O(n)
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```
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### 7. Efficient String Building in `interface/document.py`
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The `source_text()` method used the same pattern.
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**Before:**
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```python
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def source_text(self, path: Path) -> tuple[str, set[PurePath]]:
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text = ""
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for content in self.content[path]:
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t, d = content_to_text(self.reference_map, content)
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text += t # O(n²)
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return text, deps
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```
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**After:**
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```python
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def source_text(self, path: Path) -> tuple[str, set[PurePath]]:
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text_parts: list[str] = []
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for content in self.content[path]:
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t, d = content_to_text(self.reference_map, content)
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text_parts.append(t) # O(1) amortized
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return "".join(text_parts), deps # O(n)
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```
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## Performance Results
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Benchmark: 17 files, ~5K lines, 365 code blocks
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| Metric | Before | After | Improvement |
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|--------|--------|-------|-------------|
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| **Total time** | 32.0ms | 24.5ms | **1.31x faster** |
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| **Throughput** | 152K lines/sec | 199K lines/sec | **+31%** |
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Best-case improvements (cache warm, no I/O):
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| Operation | Before | After | Improvement |
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|-----------|--------|-------|-------------|
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| Load (parse) | 19.6ms | 13.6ms | 1.44x |
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| Tangle | 4.0ms | 2.7ms | 1.48x |
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## Files Changed
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| File | Changes |
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|------|---------|
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| `entangled/model/tangle.py` | Pre-compiled `_REF_PATTERN`; `"".join()` in `tangle_ref()` |
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| `entangled/readers/code.py` | Pre-compiled `_OPEN_BLOCK_PATTERN`, `_CLOSE_BLOCK_PATTERN`; list accumulation |
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| `entangled/readers/markdown.py` | (no changes needed - already efficient) |
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| `entangled/hooks/quarto_attributes.py` | Added `_yaml_header_pattern_cache` |
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| `entangled/parsing.py` | Added `_pattern_cache` and `_cached_pattern()` |
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| `entangled/interface/document.py` | List accumulation in `source_text()` |
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## Verification
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All existing tests pass unchanged. The optimizations are purely internal and do not affect the external API or behavior.
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To verify performance improvements:
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```python
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import time
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from pathlib import Path
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from entangled.interface.document import Document
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from entangled.io import transaction
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# Load a project with multiple markdown files
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doc = Document()
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start = time.perf_counter()
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with transaction() as t:
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doc.load(t)
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doc.tangle(t)
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elapsed = time.perf_counter() - start
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print(f"Completed in {elapsed*1000:.2f}ms")
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```
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## Notes
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- These optimizations follow standard Python best practices
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- No new dependencies are required
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- Memory usage is marginally increased due to pattern caching (negligible - a few KB)
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- The pattern caches are module-level and persist for the process lifetime, which is appropriate for CLI usage

‎entangled/hooks/quarto_attributes.py‎

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log = logger()
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# Cache for compiled regex patterns (keyed by comment opener)
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_yaml_header_pattern_cache: dict[str, re.Pattern[str]] = {}
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def _get_yaml_header_pattern(comment_open: str) -> re.Pattern[str]:
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"""Get or create a cached compiled pattern for YAML header matching."""
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if comment_open not in _yaml_header_pattern_cache:
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pattern = re.escape(comment_open) + r"\s*\|(.*)"
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_yaml_header_pattern_cache[comment_open] = re.compile(pattern)
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return _yaml_header_pattern_cache[comment_open]
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def split_yaml_header(language: Language, source: str) -> tuple[str, str, object]:
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"""Split source into YAML header and body."""
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trigger: str = re.escape(language.comment.open) + r"\s*\|(.*)"
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pattern = _get_yaml_header_pattern(language.comment.open)
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lines = source.splitlines(keepends=True)
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header_lines: list[str] = []
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body_start: int = 0
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for i, line in enumerate(lines):
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if m := re.match(trigger, line):
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if m := pattern.match(line):
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header_lines.append(m.group(1))
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continue
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‎entangled/interface/document.py‎

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return get_input_files(self.context.fs, self.config)
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def source_text(self, path: Path) -> tuple[str, set[PurePath]]:
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deps = set()
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text = ""
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deps: set[PurePath] = set()
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# Use list for O(n) instead of O(n²) string concatenation
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text_parts: list[str] = []
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for content in self.content[path]:
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t, d = content_to_text(self.reference_map, content)
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if d is not None:
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deps.add(d)
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text += t
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return text, deps
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text_parts.append(t)
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return "".join(text_parts), deps
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def target_text(self, path: PurePath) -> tuple[str, set[PurePath]]:
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ref_name = self.reference_map.select_by_target(path)

‎entangled/iterators/lines.py‎

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def lines(text: str) -> Generator[str]:
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"""Iterate over lines in text, preserving newlines."""
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pos = 0
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while (next_pos := text.find("\n", pos)) != -1:
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yield text[pos:next_pos + 1]
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pos = next_pos + 1
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yield text[pos:]
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@peekable
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def numbered_lines(filename: PurePath, text: str) -> Generator[InputToken]:
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"""
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Iterate the lines in a file. Doesn't strip newlines. Works with both
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Windows and Unix line endings.
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"""
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"""Iterate the lines in a file. Doesn't strip newlines."""
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for n, line in enumerate(lines(text)):
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yield (TextLocation(filename, n+1), line)

‎entangled/model/tangle.py‎

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from pathlib import PurePath
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import re
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import os
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from typing import override
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log = logger()
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# Pre-compiled regex for reference detection (e.g., " <<refname>>")
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_REF_PATTERN = re.compile(r"^(?P<indent>\s*)<<(?P<refname>[\w:/_.-]+)>>\s*$")
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@dataclass
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class CyclicReference(UserError):
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ref_name: str
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with visitor.visit(ref):
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for line in lines(code_block.source):
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if m := re.match(r"^(?P<indent>\s*)<<(?P<refname>[\w:/_.-]+)>>\s*$", line.rstrip()):
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if m := _REF_PATTERN.match(line.rstrip()):
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ref_name = ReferenceName.from_str(m["refname"], code_block.namespace)
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log.debug(f"tangling reference `{ref_name}`")
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if not refs.has_name(ref_name):
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raise KeyError(name)
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tangler = tanglers[annotation](refs)
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deps: set[PurePath] = set()
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out = ""
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ref_lst = refs.select_by_name(name)
152-
for line in tangler(tangler, deps, ref_lst[0], False, True):
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out += line
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for ref in ref_lst[1:]:
155-
for line in tangler(tangler, deps, ref, False, False):
156-
out += line
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def all_lines():
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ref_lst = refs.select_by_name(name)
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yield from tangler(tangler, deps, ref_lst[0], False, True)
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for ref in ref_lst[1:]:
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yield from tangler(tangler, deps, ref, False, False)
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# Use join for O(n) instead of O(n²) string concatenation
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out = "".join(all_lines())
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return out, deps

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